TY - GEN
T1 - Landmark data selection and unmapped obstacle detection in lidar-based navigation
AU - Joerger, Mathieu
AU - Arana, Guillermo Duenas
AU - Spenko, Matthew
AU - Pervan, Boris
N1 - Funding Information:
The authors gratefully acknowledge the National Science Foundation for supporting this research (NSF award #1637899). However, the opinions expressed in this paper do not necessarily represent those of any other organization or person.
Publisher Copyright:
© 2017 Institute of Navigation. All rights reserved.
PY - 2017
Y1 - 2017
N2 - This research establishes new methods to quantify lidar-based navigation safety in highly automated vehicle (HAV) applications. Lidar navigation requires feature extraction (FE) and data association (DA). In prior work, an FE and DA risk prediction process was developed assuming that the set of extracted features matched the set of mapped landmarks. This paper addresses these limiting assumptions by first providing the means to select a subset of feature measurements (to be used in the estimator) while accounting for all existing landmarks in the surroundings. This is achieved by employing a probabilistic lower-bound on the mean innovation vector's norm. This measure of landmark separation is used in an analytical integrity risk bound that accounts for all possible association hypotheses. Then, a solution separation algorithm is employed to detect unmapped obstacles and wrong extractions. The integrity risk bound is modified to incorporate the risk of not detecting an unwanted obstacle (UO) when one might be present. Covariance analysis, direct simulation, and preliminary testing show that selecting fewer extracted features can significantly reduce integrity risk, but can also decrease landmark redundancy, thereby reducing UO detection capability.
AB - This research establishes new methods to quantify lidar-based navigation safety in highly automated vehicle (HAV) applications. Lidar navigation requires feature extraction (FE) and data association (DA). In prior work, an FE and DA risk prediction process was developed assuming that the set of extracted features matched the set of mapped landmarks. This paper addresses these limiting assumptions by first providing the means to select a subset of feature measurements (to be used in the estimator) while accounting for all existing landmarks in the surroundings. This is achieved by employing a probabilistic lower-bound on the mean innovation vector's norm. This measure of landmark separation is used in an analytical integrity risk bound that accounts for all possible association hypotheses. Then, a solution separation algorithm is employed to detect unmapped obstacles and wrong extractions. The integrity risk bound is modified to incorporate the risk of not detecting an unwanted obstacle (UO) when one might be present. Covariance analysis, direct simulation, and preliminary testing show that selecting fewer extracted features can significantly reduce integrity risk, but can also decrease landmark redundancy, thereby reducing UO detection capability.
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U2 - 10.33012/2017.15406
DO - 10.33012/2017.15406
M3 - Conference contribution
AN - SCOPUS:85047876774
T3 - 30th International Technical Meeting of the Satellite Division of the Institute of Navigation, ION GNSS 2017
SP - 1886
EP - 1903
BT - 30th International Technical Meeting of the Satellite Division of the Institute of Navigation, ION GNSS 2017
PB - Institute of Navigation
T2 - 30th International Technical Meeting of the Satellite Division of the Institute of Navigation, ION GNSS 2017
Y2 - 25 September 2017 through 29 September 2017
ER -